Selected Talks

To access the full list of my talks, please visit my SlideShare webpage or consult my CV.

Invited Talks

•           May 30, 2024 / 9.30am-11am MT
•         Talk
•   Department of Psychology, University of Alberta, Edmonton, Alberta, Canada

Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes [1,2,3,4,5]. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms [6,7], due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain [3,8,9]. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization [10]. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development [11], and their applicability in cognitive as well as clinical neuroscience.

•           March 22, 2023 / 3pm-4pm EST
•         Seminar at the MNI Feindel Brain and Mind Lecture Series
•   McConnell Brain Imaging Centre (BIC) and Montreal Neurological Institute (The Neuro), McGill University, Montreal, Canada

Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required, by pooling data or results from different single-task studies. Meta-analyses allow the accumulation of knowledge across studies. Yet, they are typically impacted not only by inter-subject and inter-site variability but also loss of information from sparse peak-coordinate representations. In this talk, I will address a battery of studies, which combine deep phenotyping and multitask-fMRI approaches to extensively investigate the functional signatures of the different components that characterize the human behavior. First, I will describe a set of experiments, based on temporally controlled task designs, reported in [1], [2] and [3], in which we leverage a collection of source task-fMRI data from the Individual Brain Charting (IBC) dataset. The main goal herein is to investigate the feasibility of performing individual functional brain atlasing, free from inter-subject and inter-site variability, as an effort to establish an univocal relationship between functional segregation of brain regions and elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. In addition, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network. Second, I will describe our ongoing work on the quality-assessment and validation of a subset of tasks from the IBC dataset based on naturalistic stimuli using two types of encoding models: the unsupervised Fast Shared Response Model [4], and a feature-defined model based on Deep Convolutional Neural Networks [5, 6].

•           April 14, 2022 / 11am-12.30pm EST
•         Seminar
•   Institut Universitaire de Gériatrie de Montréal (IUGM), Montreal, Canada

Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required, by pooling data or results from different single-task studies. Meta-analyses allow the accumulation of knowledge across studies. Yet, they are typically impacted not only by inter-subject and inter-site variability but also loss of information from sparse peak-coordinate representations. In this talk, I will address a battery of studies, which combine deep phenotyping and multitask-fMRI approaches to extensively investigate the functional signatures of the different components that characterize the human behavior. First, I will describe a set of experiments, based on temporally controlled task designs, reported in [1], [2] and [3], in which we leverage a collection of source task-fMRI data from the Individual Brain Charting (IBC) dataset. The main goal herein is to investigate the feasibility of performing individual functional brain atlasing, free from inter-subject and inter-site variability, as an effort to establish an univocal relationship between functional segregation of brain regions and elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. In addition, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network. Second, I will describe our ongoing work on the quality-assessment and validation of a subset of tasks from IBC dataset based on naturalistic stimuli using a Fast Shared Response Model encoding experiment [4]. I will finish this presentation with some insights about the application of the aforementioned functional-atlasing techniques to probe region-specific topographies linked to a particular neurocognitive mechanism of interest.

•           November 5, 2020 / 8pm-9.30pm UTC
•         Poldrack Lab Seminar
•   Online

Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. The Individual Brain Charting (IBC) project stands for a high-resolution (1.5mm), multi-task fMRI dataset, intended to provide an objective basis for the establishment of a neurocognitive atlas based on the individual mapping of the human brain. This data collection refers to a permanent cohort during performance of a wide variety of tasks across many sessions. Data up to the third release---comprising 28 tasks---are publicly available in the OpenNeuro repository (ds002685). Derived statistical maps from the first and second releases can be found in NeuroVault (id6618) and they amount for 205 canonical contrasts described on the basis of 113 cognitive concepts taken from the Cognitive Atlas. These derivatives reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. As the dataset becomes larger and the ensuing collection of concepts gets richer, finer subject-specific, cognitive topographies can be extracted from the data. We thus explore this individual-functional-atlasing approach in order to link functional segregation of specialized brain regions to elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. Lastly, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network.

•           February 21, 2019 / 1.45pm-2.30pm JST
•         The 5th CiNet Conference: Computation and representation in brains and machines
•   CiNet Bldg., Osaka, Japan

Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has contributed to the investigation of brain regions involved in a variety of cognitive processes. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The first release of the IBC dataset consists of data acquired from thirteen participants during performance of a dozen of tasks. Raw data from this release are publicly available in the OpenNeuro repository and derived statistical maps can be found in NeuroVault. These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain. Indeed, main findings of the original studies were replicated at higher resolution. Our results thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. In addition, this dataset supports investigations using alternative approaches to group-level analysis of task-specific studies. For instance, such rich task-wise dataset can be applied to mega-analytic encoding models towards the development of a brain-atlasing framework, by systematically mapping functional signatures associated with the cognitive components of the tasks.

Talks at International Conferences

•           July 2, 2020 / 6am-7am UTC
•         Oral Session - Modeling and Analysis: Variability in Brain Activation at OHBM conference 2020
•   Online

Aims

FMRI allows for characterization of brain activations in response to behavior. However, cognitive neuroscience is limited to group-level effects on specific tasks. Pooling data from different task-fMRI studies free from inter-subject and inter-site variability is mandatory toward a fine functional profile of cognitive atoms. We present the Individual Brain Charting dataset --concerning fMRI data acquired at high resolution (1.5mm) in the same environment and cohort-- and investigate the feasibility of individual functional atlasing using a rich taskwise dataset.

Methods

Individual z-maps from the 60 main contrasts across tasks were estimated to capture significant functional signatures. These derivatives were analyzed as in Pinho et al. (2018). Besides, contrasts were decomposed using dictionary-learning into individual networks featuring neural correlates common to the tasks. To gain insights about these commonalities, we also reconstructed contrasts from the remaining ones in a cross-validation experiment. Additionally, we delineated the cognitive profile of 6 regions-of-interest and assessed whether voxels were correctly assigned to these regions across participants.

Results

Individual components were consistently mapped and tasks were well predicted from one another. Yet, scores decreased when subjects were permuted between train and test, showing that topographies are driven by subject-specific anatomo-functional characteristics. Additionally, characterization of regions-of-interest from many contrasts objectively establishes functional specialization, supported by prediction accuracies of voxel classification.

Conclusions

Successful predictions revealed the existence of a latent structure underlying different tasks, illustrating the benefit of system-level, multi-task brain mapping. Contrasts and, subsequently, individual topographies are increasing with the latest releases, allowing for better brain-atlasing frameworks.

•           June 23, 2020 / Hub 1: 3.50am-4.40am UTC, Hub 2: 10.50am-11.40am UTC, Hub 3: 7.50pm-8.40pm UTC
•         Open Data 2.0 demo at virtual OHBM OSR 2020
•   Online

We present an extension of the Individual Brain Charting (IBC) dataset –an open-access, high spatial-resolution, multitask-fMRI dataset. It is intended to support the investigation on the functional principles governing cognition in the human brain and boost the development of brain-atlasing frameworks. Data are acquired from a permanent cohort (n=12) in the same environment, in order to obtain finer cognitive topographies, free from inter-subject and inter-site variability. The second release includes new tasks on e.g. "mental-time travel", "reward", "theory-of-mind", "pain", "numerosity", "self-reference effect" and "speech recognition". They provide data featuring new psychological domains as well as data from psychological domains already covered in the first release. The dataset currently encompasses 25 tasks --most of them reproduced from previous studies-- and they amount for 205 contrasts described on the basis of 113 cognitive concepts, extracted from the Cognitive Atlas. Additionally, we also present the third release; it pertains to complex and continuous stimuli tackling the visual system and, here, we show the benefits of integrating task data concerning different experimental designs, namely temporal-models designs and naturalistic ones. Source data and derivatives are available in OpenNeuro (ds002685) and NeuroVault (id=6618), respectively. These derivatives refer to smoothed and unthresholded contrast z-maps and they reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. Besides, the new tasks complement the already existing ones such that a considerable overlap of cognitive concepts is attained, at the same time that new concepts are introduced. As the dataset becomes larger and the collection of concepts gets richer, we show that finer elementary topographies associated with such concepts can be obtained, thus, improving the atlasing of mental functions.

•           June 11, 2019 / 10.30am-11.45pm CET
•         Talk presented in "From statistical to biological validity" session at OHBM OSR 2019
•   OHBM conference 2019, Rome, Italy

Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. To date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) dataset stands for a high-resolution multi-task fMRI dataset that intends to provide a framework toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort (12 participants) performing many different tasks, free from both inter-subject and inter-site variability. The first release of the IBC dataset is already out and publicly available in the OpenNeuro (ds000244) and NeuroVault (id=4438). These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain, as the main findings of the original studies were reproduced at a high resolution. They thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. Additionally, this dataset supports the investigation of mega-analytic encoding models to be implemented in a brain-atlasing infrastructure, by systematically mapping functional signatures associated with the cognitive components of the tasks.